SS2 - Deep Learning Architectures for Visual Object Recognition

Session chairs

Description

Recently deep learning has shown its great capability for the descriptions of complexed data, and consequently achieved the state-of-the-art performance in many computer vision tasks, such as object detection, object recognition, image segmentation and so on. Object recognition is one of the most essential tasks for computer vision, which provide fundamental information for higher level image understanding tasks. Due to the variant view position, environment situation, and possible deformation of the object itself, recognition of object visually is still a challenge problem. Recently, researchers address object recognition problem with the deep learning based methods due to the rich hierarchical deep feature. However, how to design optimal deep learning neural network is still unclear. In addition, the deep learning neural network always requires a huge amount of training data to achieve proper performance of the network. All of these problems limits the widely application of deep learning networks. Based on the tasks of object detection, this special session aims to encourage original and novel ideas of the structure design of deep learning neural networks and its applications in many different situations.

The topics of interest include, but are not limited to:

Deep learning networks for object recognition;

Optimal design of deep learning networks;

Understanding of deep learning features and its applications;

Deep learning method for small sample datasets;

Transfer learning for deep learning based methods.

Paper Submission

Please submit your full paper choosing the right track on the Conference Management Toolkit (Microsoft’s CMT) site.See Paper Submission for more details. All papers must be written in English and should describe original work. The length of the paper is limited to a maximum of 6 pages (in the standard IEEE conference double column format).